Exam Professional Data Engineer topic 1 question 287 discussion

Reservations guarantee a fixed number of slots (computational resources) for BigQuery queries, ensuring a predictable monthly cost, addressing the marketing team's concern about variability.

A because allow flexibility and scaling, so setting a baseline with autoscaling ensures that the marketing team can handle their queries without large fluctuations in cost.

C seems much more robust and reliable than B. We can keep spend consistent whilst not sacrificing on performance (if we do B, once the byte scan limit has been reached, users will not be able to perform any analysis which could be detrimental to business)

This is trick question. The answer B is setting quota on bytes but it does not address the cost variability. The C will give the predictable monthly cost.

Answer appears to be C. Check the example from docs: https://cloud.google.com/bigquery/docs/reservations-workload-management#managing_your_workloads_and_departments_using_reservations

It's a treaking question but it's C, they are asking for establish a consistent Bigquery analytics spend each month, if you put 500 slots as baseline and with no autoscaling, each month they'll get the a consistent Bigquery analytics spend.

A, C and D talks about slot counts, whereas question does not talk about any such requirement, we should not make assumption on slots required or not required. Option B provides the visibility of cost to the team and can be revised as needed. So B is the right option.

The correct answer is: A. Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly. Here's the rationale: Consistent Spend with Reservation: Creating a BigQuery Enterprise reservation provides the marketing team with dedicated slots, which can help stabilize and predict their monthly costs. By having a reservation baseline of 250 slots, they are guaranteed a certain level of performance and cost each month. Autoscaling for Flexibility: The autoscaling up to 500 slots allows the team to handle spikes in demand without being constrained by the fixed slot count. Autoscaling in this scenario enables some flexibility while still providing predictable spending due to the baseline. Billing Back: The reservation model allows for internal chargeback by department based on slot usage, helping the marketing team plan a predictable budget.

Answer is B. Custom quotas are a powerful feature that allow you to set hard limits on specific resource usage. In the case of BigQuery, quotas allow you to control query usage (number of bytes processed) at a project- or user-level. Project-level custom quotas limit the aggregate usage of all users in that project, while user-level custom quotas are separately applied to each user or service account within a project. Custom quotas are relevant when you are using BigQuery’s on-demand pricing model, which charges for the number of bytes processed by each query. When you are using the capacity pricing model, you are charged for compute capacity (measured in slots) used to run queries, so limiting the number of bytes processed is less useful. By setting custom quotas, you can control the amount of query usage by different teams, applications, or users within your organization, preventing unexpected spikes in usage and costs.

Just to clarify a point of confusion: setting a quota does not affect variability (as specified in the question). It means there is a limit to the maximum but it can still vary anywhere between zero and that maximum each month. It would also prevent the marking team actually performing the queries if set too low. C is the only one that makes sense, though the question "why 500" is a valid one, all the other answers simply do not deliver the requirements.

It should be B C is a subset of B. I mean you can put custom quota == 500 slots and obviously there wont be any auto scaling. that exactly the purpose of quota

A Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly. GIves a consistent baseline cost and allocation for peak times

The objective here is not performance. It's more concerned about the spend each month. It's not about 250 slots or 500 slots. Selecting a custom quota will let you select what ever slot you want but stay consistent with it, rather than getting stick by a particular slot option.

Custom Quota If you have multiple BigQuery projects and users, you can manage costs by requesting a custom quota that specifies a limit on the amount of query data processed per day. Daily quotas are reset at midnight Pacific Time. Custom quota is proactive, so you can't run an 11 TB query if you have a 10 TB quota. Creating a custom quota on query data lets you control costs at the project level or at the user level. Project-level custom quotas limit the aggregate usage of all users in that project.

I think is A

Balance of Cost and Performance: By setting a baseline of slots to ensure a minimum cost, and enabling autoscaling for flexible scaling based on demand, we can achieve an optimal balance between cost and performance. Cost Control: Setting a maximum number of slots allows us to clearly define a cost ceiling. Benefits of Enterprise Reservations: Enterprise reservations offer a higher discount rate compared to Standard reservations, and provide more stable performance.

Yes, you can reserve slots in BigQuery for on-demand billing: Slot recommender Use the slot recommender to get cost-optimized recommendations for on-demand workloads. Capacity commitment Purchase a capacity commitment to reserve capacity for a minimum amount of time and save on costs. Capacity-based pricing Use reservations to switch to capacity-based pricing, which lets you reserve a volume of slots. You pay for that capacity continuously every second it's deployed.